Abstract
A semi-automated method for segmentation of liver nodules in Computed Tomography studies is described in this paper. The application is part of a liver cancer computer-aided diagnosis (CAD) system. Its main body consists of the three-dimensional anisotropic diffusion filtering and the adaptive region growing, supported by the fuzzy inference system. Such a workflow enables elimination of noise within the image data, enhances nodule region boundaries, and cuts ,,segmentation leaks”. The outcome is interactively presented to the physician with a possibility left to make manual adjustments. The system has been evaluated using a database of 17 abdominal Computed Tomography studies including 30 various liver nodules outlined by the radiologist, yielding 77% effectiveness (23 cases).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
World Health Organization: Cancer (2012), http://www.who.int/topics/cancer/en/
American Cancer Society: Liver Cancer Overview (2012), http://www.cancer.org/acs/groups/cid/documents/webcontent/003058-pdf.pdf
Pietka, E., Kawa, J., Spinczyk, D., et al.: Role of Radiologists in CAD Life-Cycle. European Journal of Radiology 78(2), 225–233 (2011)
Häme, Y.: Liver Tumor Segmentation Using Implicit Surface Evolution. The Midas Journal (2008), http://grand-challenge2008.bigr.nl/proceedings/pdfs/lts08/13_TKKBecs.pdf
Moltz, J., Bornemann, L., Kuhnigk, J., et al.: Advanced Segmentation Techniques for Lung Nodules, Liver Metastases, and Enlarged Lymph Nodes in CT Scans. IEEE Journal on Selected Topics in Signal Processing 3(1), 122–134 (2009)
Qi, Y., Xiong, W., Leow, W., et al.: Semi-automatic Segmentation of Liver Tumors from CT Scans Using Bayesian Rule-based 3D Region Growing. The Midas Journal (2008), http://www.comp.nus.edu.sg/~leowwk/papers/miccai2008-tumor1.pdf
Wong, D., Liu, J., Fengshou, Y., et al.: A Semi-automated Method for Liver Tumor Segmentation Based on 2D Region Growing with Knowledge-based Constraints. The Midas Journal (2008), http://grand-challenge2008.bigr.nl/proceedings/pdfs/lts08/09_NUS-I2R-team1.pdf
Li, K., Jolly, M.: Simultaneous Detection of Multiple Elastic Surfaces with Application to Tumor Segmentation in CT Images. In: Proc. SPIE, vol. 6914 (2008)
Lu, R., Marziliano, P., Thng, C.: Live Tumor Volume Estimation by Semi-Automatic Segmentation Method. In: Proc. IEEE EMBS, pp. 3296–3299 (2005)
Stawiaski, J., Decencière, E., Bidault, F.: Interactive Liver Tumor Segmentation Using Graph-cuts and Watershed. The Midas Journal (2008), http://grand-challenge2008.bigr.nl/proceedings/pdfs/lts08/02_cmm.pdf
Zhou, J., Xiong, W., Tian, Q., et al.: Semi-automatic Segmentation of 3D Liver Tumors from CT Scans Using Voxel Classification and Propagational Learning. The Midas Journal (2008), http://grand-challenge2008.bigr.nl/proceedings/pdfs/lts08/10_NUS-I2R-team2.pdf
Kubota, T.: Efficient Automated Detection and Segmentation of Medium and Large Liver Tumors: CAD Approach. The Midas Journal (2008), http://grand-challenge2008.bigr.nl/proceedings/pdfs/lts08/04_Goma.pdf
Perona, P., Malik, J.: Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)
Koenderink, J.: The Structure of Images. Biological Cybernetics 50, 363–370 (1984)
Gonzalez, R., Woods, R.: Digital Image Processing. Prentice Hall, Upper Saddle River (2002)
Mamdani, E., Assilan, S.: An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller. International Journal of Man-Machine Studies 7(1), 1–13 (1975)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Badura, P., Pietka, E. (2012). 3D Fuzzy Liver Tumor Segmentation. In: Piętka, E., Kawa, J. (eds) Information Technologies in Biomedicine. Lecture Notes in Computer Science(), vol 7339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31196-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-31196-3_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31195-6
Online ISBN: 978-3-642-31196-3
eBook Packages: Computer ScienceComputer Science (R0)